Use Codex to map Reddit complaints by theme
A developer turned Reddit complaint research into a Codex skill that checks communities, gathers quotes, and sorts issues into evidence-backed groups.
- What they did
- They converted a customer-research process for Reddit into a Codex skill rather than wiring together a separate agent setup. Codex took on the thinking, web browsing, tool calls, file access, and chat, while the skill spelled out the method and waited for human sign-off before the main research stage. The only Python pieces were narrow helpers for repeatable jobs like input checks, scoring, normalizing links, removing repeats, and building artifacts.
- How it went
- The process yielded the same style of organized output the earlier setup was supposed to produce: community discovery, activity checks, quote collection, issue clustering, and review of commercial intent. No benchmark figures or direct comparison were provided, so the main benefit was a cleaner architecture and the ability to continue the work in the same conversation.
- Tips
- Make the workflow steps explicit and keep the approval stop before any action that cannot be undone. Since this depends on Codex as the execution layer, it is a demonstration of a harnessed setup rather than a portable agent.
Try it yourself with Codex
I want to study how people complain about [topic or product area] on Reddit. Start by listing possible subreddits and stop so I can approve the plan before you dig deeper. Once I say go, check which communities are active and relevant, pull representative comments with source links, sort the complaints into patterns, and point out any signs that users might pay for a fix. Put the finished report in [file path] and order the themes by how strong the evidence is.
Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.
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